Real Time Market (RTM) at Indian power exchanges: Need, short term assessment and opportunities

Agrawal, Atul

doi:10.1016/j.enpol.2022.112810

Cited by 6 publications

(2 citation statements)

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“…The generation load aggregator needs the corresponding generation and consumption plan submitted in the day-ahead market, and the inequality between the planned generation and the actual volume on day 2 caused by the forecast error needs to be purchased in the real-time market (Lankeshwara et al, 2022). Electricity purchase prices in the real-time market are generally higher than those in the day-ahead market, and electricity sales prices are generally lower than those in the day-ahead market (Agrawal, 2022), so forecast errors can lead to higher final transaction costs. In summary, the scheduling Imbalance power generated by real-time power markets.…”

Section: Comparison Of Optimization Modelsmentioning

confidence: 99%

“…The time-sharing tariff mechanism between the generation load aggregator and the external grid shown in Figure 7 allows the generation load aggregator to use energy storage to utilize the power purchased in the valley hours in the peak hours under the condition that the energy storage portion of the generation load aggregator needs to meet the constraint (Agrawal, 2022). Based on this premise, the boundary conditions for the use of energy storage for peak shaving by generation load aggregators can be further deduced; in other words, generation load aggregators will use energy storage only under the condition that energy storage can reduce the operating cost of the system.…”

Section: Energy Storage Scheduling Boundary Conditionsmentioning

confidence: 99%

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Economic dispatch of generation load aggregators based on two-stage robust optimization

Zhang,

Tian,

Zhao

et al. 2023

Front. Energy Res.

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Introduction: In recent years, with the rapid development of renewable energy generation, the stability of the power grid has been greatly reduced. In response to this problem, integrating the user side transferable load into the power market has become the key to the development of future power grid. At present, large transferable loads have entered the electricity market in some pilot areas of China, but the relevant research on small and medium-sized transferable users entering the electricity market is still few.Methods: This paper proposes the concept of generation load aggregators. A two-stage generation load aggregator robust optimization model is developed to obtain the scheduling scheme with the lowest operating cost under the worst scenario. The model consists of distributed renewable power, transferable load, self-provided power, energy storage, etc. Uncertainties of renewable energy and load are introduced in the model. By using the column constraint generation algorithm and strong pairwise theory, the original problem is decomposed into the main problem and sub-problems to be solved alternately, so as to obtain the scheduling scheme with the lowest operating cost in the worst scenario under different conservatism.Results: The solved results are compared with those without the generation load aggregator, illustrating the role of the generation load aggregator in relieving peak and valley pressure on the grid from the load side, reducing the cost of electricity for loads, and promoting the consumption of renewable energy. The comparison with the deterministic optimization algorithm shows a significant decrease in the total cost and validates the performance of the selected solution algorithm. The boundary conditions for the use of energy storage by generation load aggregators for peak and valley reduction under the time-sharing tariff mechanism are also derived.Discussion: This study can provide reference for the investors of generation load aggregators when planning whether to install energy storage or the scale of energy storage, and also help the power market management department to design a reasonable incentive mechanism.

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Section: Comparison Of Optimization Modelsmentioning

confidence: 99%

Section: Energy Storage Scheduling Boundary Conditionsmentioning

confidence: 99%